Meeting-6 SAMPLING DESIGN

The Nature of Sampling Population: total collection of elements about which we wish to make some inferences Population element: the individual participant or object on which the measurement is taken Sampling: some of the elements of population Sample frame: the listing of all population elements from which the sample will be drawn

Why Sample? Lower cost Greater accuracy of results Greater speed of data collection Availability of population elements

What is a Good Sample? Accurate: absence of bias Precise estimate: small standard error of estimate

Steps in Sampling Design What is the target population? Should be defined clearly What are the parameters of interest? Summary descriptors of variables of interest in the population What is the sampling frame? Should complete and correct What is the appropriate sampling method? Probability or nonprobability sampling What size sample is needed?

Parameters of Interest

What Size Sample Is Needed? Some principles : The greater the dispersion or variance , the larger the sample The greater precision of the estimate, the larger the sample The narrower or smaller the error range, the larger the sample The higher the confidence level, the larger the sample The greater the number of subgroups of interest within sample, the larger the sample The lower cost/respondent, the larger the sample

Types of Sampling Design Probability Sampling  based on the concept of random selection Simple random Systematic Stratified Cluster Double Nonprobability Sampling  arbitrary and subjective Convenience (unrestricted of element selection) Purposive: Judgment, Quota Snowball

Probability Sampling Designs 1. Simple random sampling: Special case in case which each population element has a known and equal chance of selection Easy to be implemented by random number or using computer (SPSS)

How to Choose a Random Sample

How to Choose a Random Sample

Probability Sampling Designs Systematic Sampling Every kth element in the population is sampled, beginning with a random start of an element in the range of 1 to kth k = Skip interval = Population size Sample size

Probability Sampling Designs 3. Stratified Random Sampling Population is devided into strata, then a simple random sample can be taken within each stratum Process for drawing sample: Determine the variable to use for stratification Determine the proportions of the stratification variables Select proportionate or disproportionate stratification Devide the sampling frame into separate frame for each stratum Randomize the elements within each stratum Follow random or systematic procedure to draw the sample from each stratum

Probability Sampling Designs 4. Cluster Sampling Population is devided into subgroups based on area or cluster. Few clusters (subgroups) then are selected based on some criterion and finally elements within each cluster is chosen randomly The reason of using this method: Efficiency Unavailability sampling frame for individual elements

Probability Sampling Designs

Probability Sampling Designs Section 1 Section 2 Section 3 Section 5 Section 4

Probability Sampling Designs

Probability Sampling Designs Double Sampling Selecting subsample from sample which taken before for further study Called as sequential sampling or multiphase sampling

Probability Sampling Designs

Nonprobability Sampling Reasons to use Procedure satisfactorily meets the sampling objectives Lower Cost Limited Time Not as much human error as selecting a completely random sample The population elements is not available

Nonprobability Sampling 1. Convenience Sampling The sampling procedure used to obtain those units or people most conveniently available Researchers have the freedom to choose whomever they find 2. Purposive Sampling The sampling procedure in which an experienced research selects the sample based on some appropriate characteristic of sample members… to serve a purpose 3. Snowball Sampling The sampling procedure in which the initial respondents are chosen by probability or non-probability methods, and then additional respondents are obtained by information provided by the initial respondents

VIDEO Using random numbers Simple random sampling Systematic sampling Stratified sampling Cluster sampling